Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNN)


The Ultimate Training for Recurrent Neural Networks (RNN)

Classes : 20                  Days : 2 months                  Duration : Weekdays / Weekends

RNNs works on the principle of saving the output of a particular layer and feeding this back to the input in order to predict the output of the layer. Generally they are the queen of algorithms for Speech Recognition, Voice Recognition, Time Series Prediction, Natural Language Processing and Machine Translation (i.e. Google Translate).

In this training, we shall use Keras with TensorFlow as its backend to create a RNN model. You shall learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages. Exploring how information flows through a RNN, you`ll use a Keras RNN model to perform sentiment classification.

During the course you will learn how to prepare data for the multi-class classification task, as well as the differences between multi-class classification and binary classification (sentiment analysis).

This course provides an in-depth look at RNNs in machine learning, giving you the knowledge to build your skills in this area. This is an enlightening course. Why Wait? Register today.

Experts from the field of Maths, Data Science and Management, each with over 25 years of International experience working in EU/US/Australia

What you'll learn
:- Get a solid understanding of Recurrent Neural Networks (RNN) and Deep Learning
:- Learn usage of Keras and Tensorflow libraries
:- Understand the business scenarios where Recurrent Neural Networks (RNN) is applicable
:- Building and Train an Recurrent Neural Networks (RNN) in Python
:- Use Recurrent Neural Networks (RNN) to make predictions

Who this course is for:
:- People pursuing a career in Data Science
:- Anyone curious to master RNN from Beginner level in short span of time
:- Data Analysts and Engineers
:- University Students
:- Scientists and Researchers

:- You are hands-on with Machine Learning using Python.
:- You have theoretical knowledge of Artificial Neural Networks (ANN)
:- You have a genuine interest in RNN.

:- Introduction to RNNs
:- How RNN Works
:- Types of RNNs
:- CNN vs RNN
:- Limitations of RNN
:- RNN Architecture
:- GRU
:- Fine Tuning Models and Hyperparameters
:- Activation Functions
:- Basics of NLP
:- Text Processing
:- Language Model and Sequence Generation
:- Vanishing Gradients
:- Bidirectional RNNs
:- Deep RNNs


Fabulous NLP + ML course

I have eleven plus years of experience taking training courses. I do not usually complete surveys.
Your instructor was excellent, the best I've experienced on a software subject, and I couldn't imagine him doing a better job of seamlessly walking students through a breadth of information for such complex subject like AI and ML. he did a fabulous job pacing everything and addressing student questions. I am very impressed.


Excellent ML course!

The course was well structured and easy to understand. Good pace of learning.
The institute believes to provide knowledge as well as guidance in detail to each & every student.
I completed my ML course from the institute. Their international exp does help a lot !
Thanks for the training sir.

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